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# slides_lecture16 - ECON 103, Lecture 16: Autocorrelation...

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ECON 103, Lecture 16: Autocorrelation Maria Casanova June 4th (version 1) Maria Casanova Lecture 16

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Requirements for this lecture: Sections 14.1 and 14.2 of Stock and Watson Maria Casanova Lecture 16
0. Introduction This lecture coves the following topics: What is autocorrelation? What are the consequences of autocorrelation? How can we detect autocorrelation? What can we do about it? Maria Casanova Lecture 16

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1. Deﬁnition Time series data are data collected for a single entity at multiple points in time. For example, a series of observations Y 1 , Y 2 , ..., Y T representing the GDP of a country in diﬀerent years would be time-series data. Autocorrelation or serial correlation occurs in time-series data when: E ( ε t , ε s ) 6 = 0 for t 6 = s Because there is persistence in economic behavior over time, time series data on GDP show autocorrelation. Maria Casanova Lecture 16
Until now we have worked with cross-sectional data (e.g. data for diﬀerent individuals/states). Our objective has been to measure the eﬀect that some variable X had on a variable Y which varied for diﬀerent entities in our sample. Our objective is still the same with time series data: to measure the eﬀect of a variable X on a variable Y , where X and Y refer always to the same entity (individual, state, country, etc.) but vary across time. In other words, we’ll want to estimate

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## This note was uploaded on 02/04/2010 for the course ECON 103 taught by Professor Sandrablack during the Spring '07 term at UCLA.

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slides_lecture16 - ECON 103, Lecture 16: Autocorrelation...

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